Credit Risk Scorecard

Credit Risk Scorecard

Credit risk modelling is an approach to determine the probability of default of a customer. Credit scoring is based on analysing customer’s personal information, transactional information, last premiums and payments made and many more variables and the outcome is a score reflecting the creditworthiness of the customer.

There are various credit scoring techniques which can be used for building collection scorecard, behavioural scorecard, application scorecard, cross-sell and upsell models, customer acquisition and retention strategies.

Demand Forecasting, Customer Segmentation, Higher Campaign ROI, Predictive Attrition Modelling, Cross-Sell Modelling

Impact

Improved strategies across all functions

Reduced credit losses.

Enable decision making at different stages of customer lifecycle.

Strengthen enterprise-wide compliance programs.

What we do

Data Sourcing and Data Pre-processing

Understand the data schema and data dictionary.
Access data quality gaps and data sufficiency
Perform vintage and roll-rate analysis of needed.

Data Transformation

Perform variable transformation.
Correlation tests for multicollinearity.
Feature selection based on variable significance and information value.

Model Fitting & Validation

Model methodology specification
Model implementation
Build machine learning risk scoring classifier model

Risk Scorecard Validation

Carry out scorecard validation and out pf time validation
Perform reject inferencing, if needed

Monitor Model performance

Review Model approach
Access model implementation

Want to learn more about TransOrg’s value proposition, solution methodology and implementation approach?

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Case Studies

Increased customer’s credit card spend through data driven marketing campaigns for a large bank

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